mxComputeNewtonRaphson: Optimize parameters using the Newton-Raphson algorithm

In OpenMx: Extended Structural Equation Modelling

Optimize parameters using the Newton-Raphson algorithm

Description

This optimizer requires analytic 1st and 2nd derivatives of the fit function. Box constraints are supported. Parameters can approach box constraints but will not leave the feasible region (even by some small epsilon>0). Non-finite fit values are interpreted as soft feasibility constraints. That is, when a non-finite fit is encountered, line search is continued after the step size is multiplied by 10%. Comprehensive diagnostics are available by increasing the verbose level.

Usage

mxComputeNewtonRaphson(
  freeSet = NA_character_,
  ...,
  fitfunction = "fitfunction",
  maxIter = 100L,
  tolerance = 1e-12,
  verbose = 0L
)

Arguments

freeSet	names of matrices containing free variables
...	Not used. Forces remaining arguments to be specified by name.
fitfunction	name of the fitfunction (defaults to 'fitfunction')
maxIter	maximum number of iterations
tolerance	optimization is considered converged when the maximum relative change in fit is less than tolerance
verbose	integer. Level of run-time diagnostic output. Set to zero to disable

References

Luenberger, D. G. & Ye, Y. (2008). Linear and nonlinear programming. Springer.

OpenMx documentation built on Oct. 19, 2024, 9:06 a.m.